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Knowl. Discov. Data"],"published-print":{"date-parts":[[2021,6,28]]},"abstract":"<jats:p>\n            Crowd flow prediction is an essential task benefiting a wide range of applications for the transportation system and public safety. However, it is a challenging problem due to the complex spatio-temporal dependence and the complicated impact of urban structure on the crowd flow patterns. In this article, we propose a novel framework, 3-\n            <jats:bold>D<\/jats:bold>\n            imensional\n            <jats:bold>G<\/jats:bold>\n            raph\n            <jats:bold>C<\/jats:bold>\n            onvolution\n            <jats:bold>N<\/jats:bold>\n            etwork (3DGCN), to predict citywide crowd flow. We first model it as a dynamic spatio-temporal graph prediction problem, where each node represents a region with time-varying flows, and each edge represents the origin\u2013destination (OD) flow between its corresponding regions. As such, OD flows among regions are treated as a proxy for the spatial interactions among regions. To tackle the complex spatio-temporal dependence, our proposed 3DGCN can model the correlation among graph spatial and temporal neighbors simultaneously. To learn and incorporate urban structures in crowd flow prediction, we design the GCN aggregator to be learned from both crowd flow prediction and region function inference at the same time. Extensive experiments with real-world datasets in two cities demonstrate that our model outperforms state-of-the-art baselines by 9.6%\u223c19.5% for the next-time-interval prediction.\n          <\/jats:p>","DOI":"10.1145\/3451394","type":"journal-article","created":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T14:02:52Z","timestamp":1624888972000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":53,"title":["3DGCN: 3-Dimensional Dynamic Graph Convolutional Network for Citywide Crowd Flow Prediction"],"prefix":"10.1145","volume":"15","author":[{"given":"Tong","family":"Xia","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junjie","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pan","family":"Hui","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Helsinki, and the Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Funing","family":"Sun","sequence":"additional","affiliation":[{"name":"Tencent Inc., Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diansheng","family":"Guo","sequence":"additional","affiliation":[{"name":"Tencent Inc., Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Depeng","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,6,28]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/561899"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 2nd International Conference on Learning Representations.","author":"Bruna Joan","year":"2014","unstructured":"Joan Bruna , Wojciech Zaremba , Arthur Szlam , and Yann Lecun . 2014 . 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